分析阿拉伯语社交平台心理议题言论，发现三类障碍患者用语差异。
Understanding the Sociocultural Dimensions of Mental Health Discourse in Arabic-Language X Communities
- 用GPT-4识别8147条推文作者的真实经历，构建阿拉伯语心理话语数据集。
- 发现双相障碍发言含更多宗教与医学词汇，边缘型人格障碍侧重关系与情绪表达。
- 提供可复用的生成式语言模型辅助披露识别流程和文化关键词框架。
计算心理健康研究长期聚焦英语人群，阿拉伯语语境下的讨论相对不足。本研究基于607名用户的8147条推文，通过GPT-4.1个人经历披露识别管道，筛选出三个特定病症（边缘型人格障碍、双相障碍、注意力缺陷多动障碍）的阿拉伯语社交平台（原推特）社区用户。利用多领域文化关键词框架，分析其话语特征。结果显示：双相障碍相关推文包含更多宗教与医学词汇；边缘型人格障碍推文更常出现关系、身份认同与情绪困扰相关词；而注意力缺陷多动障碍推文则更关注实际症状与药物管理。这些模式为假设生成而非验证结论，因数据集在病症间不平衡，部分子语料时间集中，且关键词框架仅为初步操作化，尚未经过验证。论文贡献了可复用的大型语言模型辅助披露识别流程及探索性文化关键词框架。
原文摘要 · Abstract (English)
Computational mental health research has predominantly centered on English-speaking populations, leaving Arabic-language discourse comparatively under-examined. We present an exploratory computational study of 8,147 tweets from 607 users classified by a GPT-4.1 personal-disclosure pipeline as likely lived-experience authors in three condition-specific Arabic-language X (formerly Twitter) Communities. We focus on discourse related to borderline personality disorder (BPD), bipolar disorder, and ADHD, and characterize community-associated linguistic patterns using a multi-domain cultural keyword framework. The results suggest that in this corpus, Bipolar tweets contain more religious and medical vocabulary, BPD tweets contain more relational, identity, and emotional-distress vocabulary, and ADHD tweets more often focus on practical symptoms and medication management. We treat these patterns as hypothesis-generating rather than confirmatory because the corpus is imbalanced across conditions, some subcorpora are temporally concentrated, and the keyword framework is an initial operationalization rather than a validated measurement instrument. The paper contributes a reusable LLM-assisted personal-disclosure pipeline and an exploratory cultural keyword framework for Arabic mental health discourse.
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